OpenRaiser/Intern-Atlas huggingface dataset:
Context-aware Human Intent Inference for Improving Human Machine Cooperation
Venue: arXiv cs.HC 2018
Year: 2018

The ability of human beings to precisely recog- nize others intents is a significant mental activity in reasoning about actions, such as, what other people are doing and what they will do next. Recent research has revealed that human intents could be inferred by measuring human cognitive activities through heterogeneous body and brain sensors (e.g., sensors for detecting physiological signals like ECG, brain signals like EEG and IMU sensors like accelerometers and gyros etc.). In this proposal, we aim at developing a computa- tional framework for enabling reliable and precise real-time human intent recognition by measuring human cognitive and physiological activities through the heterogeneous body and brain sensors for improving human machine interactions, and serving intent-based human activity prediction.
Multi-Hop Routing in Covert Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In covert communication, Alice tries to communicate with Bob without being detected by a warden Willie. When the distance between Alice and Bob becomes large compared to the distance between Alice and Willie(s), the performance of covert communication will be degraded. In this case, multi-hop message transmission via intermediate relays can help to improve performance. Hence, in this work multi-hop covert communication over a moderate size network and in the presence of multiple collaborating Willies is considered. The relays can transmit covertly using either a single key for all relays, or different independent keys at the relays. For each case, we develop efficient algorithms to find optimal paths with maximum throughput and minimum end-to-end delay between Alice and Bob. As expected, employing multiple hops significantly improves the ability to communicate covertly versus the case of a single-hop transmission. Furthermore, at the expense of more shared key bits, analytical results and numerical simulations demonstrate that multi-hop covert communication with different independent keys at the relays has better performance than multi-hop covert communication with a single key.
Hardware Trojan Detection through Information Flow Security Verification
Venue: arXiv cs.CR 2018
Year: 2018

Semiconductor design houses are increasingly becoming dependent on third party vendors to procure intellectual property (IP) and meet time-to-market constraints. However, these third party IPs cannot be trusted as hardware Trojans can be maliciously inserted into them by untrusted vendors. While different approaches have been proposed to detect Trojans in third party IPs, their limitations have not been extensively studied. In this paper, we analyze the limitations of the state-of-the-art Trojan detection techniques and demonstrate with experimental results how to defeat these detection mechanisms. We then propose a Trojan detection framework based on information flow security (IFS) verification. Our framework detects violation of IFS policies caused by Trojans without the need of white-box knowledge of the IP. We experimentally validate the efficacy of our proposed technique by accurately identifying Trojans in the trust-hub benchmarks. We also demonstrate that our technique does not share the limitations of the previously proposed Trojan detection techniques.
GPU Accelerated Self-join for the Distance Similarity Metric
Venue: arXiv cs.DC 2018
Year: 2018

The self-join finds all objects in a dataset within a threshold of each other defined by a similarity metric. As such, the self-join is a building block for the field of databases and data mining, and is employed in Big Data applications. In this paper, we advance a GPU-efficient algorithm for the similarity self-join that uses the Euclidean distance metric. The search-and-refine strategy is an efficient approach for low dimensionality datasets, as index searches degrade with increasing dimension (i.e., the curse of dimensionality). Thus, we target the low dimensionality problem, and compare our GPU self-join to a search-and-refine implementation, and a state-of-the-art parallel algorithm. In low dimensionality, there are several unique challenges associated with efficiently solving the self-join problem on the GPU. Low dimensional data often results in higher data densities, causing a significant number of distance calculations and a large result set. As dimensionality increases, index searches become increasingly exhaustive, forming a performance bottleneck. We advance several techniques to overcome these challenges using the GPU. The techniques we propose include a GPU-efficient index that employs a bounded search, a batching scheme to accommodate large result set sizes, and a reduction in distance calculations through duplicate search removal. Our GPU self-join outperforms both search-and-refine and state-of-the-art algorithms.
Are Donation Badges Appealing? A Case Study of Developer Responses to Eclipse Bug Reports
Venue: arXiv cs.SE 2018
Year: 2018

Eclipse, an open source software project, acknowledges its donors by presenting donation badges in its issue tracking system Bugzilla. However, the rewarding effect of this strategy is currently unknown. We applied a framework of causal inference to investigate relative promptness of developer response to bug reports with donation badges compared with bug reports without the badges, and estimated that donation badges decreases developer response time by a median time of about two hours. The appearance of donation badges is appealing for both donors and organizers because of its practical, rewarding and yet inexpensive effect.
Importance in systems with interval decisions
Venue: arXiv cs.GT 2018
Year: 2018

Given a system where the real-valued states of the agents are aggregated by a function to a real-valued state of the entire system, we are interested in the influence or importance of the different agents for that function. This generalizes the notion of power indices for binary voting systems to decisions over interval policy spaces and has applications in economics, engineering, security analysis, and other disciplines. Here, we study the question of importance in systems with interval decisions. Based on the classical Shapley-Shubik and Penrose-Banzhaf index, from binary voting, we motivate and analyze two importance measures. Additionally, we present some results for parametric classes of aggregation functions.
Algorithmic Differentiation for Domain Specific Languages
Venue: arXiv cs.MS 2018
Year: 2018

Algorithmic Differentiation (AD) can be used to automate the generation of derivatives in arbitrary software projects. This will generate maintainable derivatives, that are always consistent with the computation of the software. If a domain specific language (DSL) is used in a software the state of the art approach is to differentiate the DSL library with the same AD tool. The drawback of this solution is the reduced performance since the compiler is no longer able to optimize the e.g. SIMD operations. The new approach in this paper integrates the types and operations of the DSL into the AD tool. It will be an operator overloading tool that is generated from an abstract definition of a DSL. This approach enables the compiler to optimize again e.g. for SIMD operation since all calculations are still performed with the original data types. This will also reduce the required memory for AD since the statements inside the DLS implementation are no longer seen by the AD tool. The implementation is presented in the paper and first results for the performance of the solution are presented.
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Venue: arXiv cs.CR 2018
Year: 2018

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software. However, it has also been shown that machine learning and deep neural networks can be fooled by evasion attacks (also referred to as adversarial examples), i.e., small changes to the input data that cause misclassification at test time. In this work, we investigate the vulnerability of malware detection methods that use deep networks to learn from raw bytes. We propose a gradient-based attack that is capable of evading a recently-proposed deep network suited to this purpose by only changing few specific bytes at the end of each malware sample, while preserving its intrusive functionality. Promising results show that our adversarial malware binaries evade the targeted network with high probability, even though less than 1% of their bytes are modified.
Cross-contextual use of integrated information systems
Venue: arXiv cs.CY 2018
Year: 2018

Large-scale organizations are increasingly promoting more collaborative and collective work practices across organizational boarders. A predominant way to achieve better collaboration in large- scale heterogeneous contexts is to establish an integrated and standardized technological infrastructure. Ethnographically inspired studies, on the other hand, have challenged such perspective and illustrated that generic technology does not fit in local contexts and needs to be worked-around. Similarly, this paper empirically exemplifies local workarounds and illustrates ongoing and persistently imperfect integration of a collaborative infrastructure in a global oil and gas company. More importantly, however, our analysis focuses on how integrated technology is used across contexts. We illustrate how local workarounds, as a result of tight technological integration, shape use patterns across contexts. Integrated systems establish interdependencies across contexts, thus, the use implies cross-contextual rather than local enactment. Since the trajectory of enactment is influenced by cross-contextual constrains, our study is addressing the existing overemphasis on studying/analysing the use of technology in isolated local contexts. Practically, our study suggests considering workarounds as an intrinsic part of every day work, which should be calculated as additional costs of making the generic technology to work in practice.
Living with technology
Venue: arXiv cs.CY 2018
Year: 2018

Our use of IT has, broadly speaking, until recently evolved around work-oriented tasks. This is or, indeed, has been changing. IT has moved beyond the workplace and into leisure, entertainment, games and our homes. It has in short moved into our everyday life. My retired father and oldest daughter use it. Use is no longer delineated but seeps into and get intervowen with a number of my activities: while paying for my groceries, planning this summer vacation and so forth.
Wireless Energy Transfer to a Pair of Energy Receivers using Signal Strength Feedback
Venue: arXiv cs.IT 2018
Year: 2018

This paper focuses on wireless energy transfer (WET) to a pair of low complex energy receivers (ER), by only utilizing received signal strength indicator (RSSI) values that are fed back from the ERs to the energy transmitter (ET). Selecting the beamformer that maximizes the total average energy transfer between the ET and the ERs, while satisfying a minimum harvested energy criterion at each ER, is studied. This is a nonconvex constrained optimization problem which is difficult to solve analytically. Also, any analytical solution to the problem should only consists of parameters that the ET knows, or the ET can estimate, as utilizing only RSSI feedback values for channel estimation prohibits estimating some channel parameters. Thus, the paper focuses on obtaining a suboptimal solution analytically. It is proven that if the channels between the ET and the ERs satisfy a certain sufficient condition, this solution is in fact optimal. Simulations show that the optimality gap is negligibly small as well. Insights into a system with more than two ERs are also presented. To this end, it is highlighted that if the number of ERs is large enough, it is possible to always find a pair of ERs satisfying the sufficient condition, and hence, a pairwise scheduling policy that does not violate optimality can be used for the WET.
Analyzing the network structure and gender differences among the members of the Networked Knowledge Organization Systems (NKOS) community
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, we analyze a major part of the research output of the Networked Knowledge Organization Systems (NKOS) community in the period 2000 to 2016 from a network analytical perspective. We focus on the papers presented at the European and U.S. NKOS workshops and in addition four special issues on NKOS in the last 16 years. For this purpose, we have generated an open dataset, the "NKOS bibliography" which covers the bibliographic information of the research output. We analyze the co-authorship network of this community which results in 123 papers with a sum of 256 distinct authors. We use standard network analytic measures such as degree, betweenness and closeness centrality to describe the co-authorship network of the NKOS dataset. First, we investigate global properties of the network over time. Second, we analyze the centrality of the authors in the NKOS network. Lastly, we investigate gender differences in collaboration behavior in this community. Our results show that apart from differences in centrality measures of the scholars, they have higher tendency to collaborate with those in the same institution or the same geographic proximity. We also find that homophily is higher among women in this community. Apart from small differences in closeness and clustering among men and women, we do not find any significant dissimilarities with respect to other centralities.
The Challenge of Crafting Intelligible Intelligence
Venue: arXiv cs.AI 2018
Year: 2018

Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and controlling otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This paper argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.
Neural Conditional Gradients
Venue: arXiv cs.LG 2018
Year: 2018

The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.
Space-Efficient Bimachine Construction Based on the Equalizer Accumulation Principle
Venue: arXiv cs.FL 2018
Year: 2018

Algorithms for building bimachines from functional transducers found in the literature in a run of the bimachine imitate one successful path of the input transducer. Each single bimachine output exactly corresponds to the output of a single transducer transition. Here we introduce an alternative construction principle where bimachine steps take alternative parallel transducer paths into account, maximizing the possible output at each step using a joint view. The size of both the deterministic left and right automaton of the bimachine is restricted by $2^{\vert Q\vert}$ where $\vert Q\vert$ is the number of transducer states. Other bimachine constructions lead to larger subautomata. As a concrete example we present a class of real-time functional transducers with $n+2$ states for which the standard bimachine construction generates a bimachine with at least $Θ(n!)$ states whereas the construction based on the equalizer accumulation principle leads to $2^n + n +3$ states. Our construction can be applied to rational functions from free monoids to "mge monoids", a large class of monoids including free monoids, groups, and others that is closed under Cartesian products.
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings
Venue: arXiv cs.CV 2018
Year: 2018

A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings from a FaceNet model were used as the features to describe an individual's face. A user reviewed 8,545 online dating profiles. For each reviewed online dating profile, a feature set was constructed from the profile images which contained just one face. Two approaches are presented to go from the set of features for each face, to a set of profile features. A simple logistic regression trained on the embeddings from just 20 profiles could obtain a 65% validation accuracy. A point of diminishing marginal returns was identified to occur around 80 profiles, at which the model accuracy of 73% would only improve marginally after reviewing a significant number of additional profiles.
Topical Community Detection in Event-based Social Network
Venue: arXiv cs.SI 2018
Year: 2018

Event-based services have recently witnessed a rapid growth driving the way people explore and share information of interest. They host a huge amount of users' activities including explicit RSVP, shared photos, comments and social connections. Exploiting these activities to detect communities of similar users is a challenging problem. In reality, a community in event-based social network (ESBN) is a group of users not only sharing common events and friends, but also having similar topical interests. However, such community could not be detected by most of existing methods which mainly draw on link analysis in the network. To address this problem, there is a need to capitalize on the semantics of shared objects along with the structural properties, and to generate overlapping communities rather than disjoint ones. In this paper, we propose to leverage the users' activities around events with the aim to detect communities based on topical clustering and link analysis that maximize a new form of semantic modularity. We particularly highlight the difference between online and offline social interactions, and the influence of event categories to detect communities. Experimental results on real datasets showed that our approach was able to detect semantically meaningful communities compared with existing state of the art methods.
A Privacy Scheme for Monitoring Devices in the Internet of Things
Venue: arXiv cs.NI 2018
Year: 2018

Sufficiently strong security and privacy mechanisms are prerequisite to amass the promising benefits of the IoT technology and to incorporate this technology into our daily lives. This paper introduces a novel approach to privacy in networks, an approach which is especially well matched with the IoT characteristics. Our general approach is based on continually changing the identifying attributes of IoT nodes. In particular, the scheme proposed in this work is based on changing the IoT nodes' IP addresses, and because the changing patterns of the IP addresses appear random to a non-intended observer, an adversary is unable to identify the source or destination of a particular transmission. Thus, packets that carry information generated by a particular node cannot be linked together. The scheme offers additional security benefits, including DoS mitigation, is relatively easy to implement, and requires no changes to the existing networking infrastructure. We discuss the details of the implementation of the scheme and evaluate its performance.
Dissimilarity-based representation for radiomics applications
Venue: arXiv cs.CV 2018
Year: 2018

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or protein data. However, most of the classification studies in radiomics report the use of feature selection methods without identifying the machine learning challenges behind radiomics. In this paper, we first show that the radiomics problem should be viewed as an high dimensional, low sample size, multi view learning problem, then we compare different solutions proposed in multi view learning for classifying radiomics data. Our experiments, conducted on several real world multi view datasets, show that the intermediate integration methods work significantly better than filter and embedded feature selection methods commonly used in radiomics.
Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing
Venue: arXiv cs.IR 2018
Year: 2018

This paper will explore the use of autoencoders for semantic hashing in the context of Information Retrieval. This paper will summarize how to efficiently train an autoencoder in order to create meaningful and low-dimensional encodings of data. This paper will demonstrate how computing and storing the closest encodings to an input query can help speed up search time and improve the quality of our search results. The novel contributions of this paper involve using the representation of the data learned by an auto-encoder in order to augment our search query in various ways. I present and evaluate the new gradient search augmentation (GSA) approach, as well as the more well-known pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve the performance of the TF-IDF based information retrieval system, and PRF combined with GSA works best overall for the systems compared in this paper.
Spanning Trees With Edge Conflicts and Wireless Connectivity
Venue: arXiv cs.NI 2018
Year: 2018

We introduce the problem of finding a spanning tree along with a partition of the tree edges into fewest number of feasible sets, where constraints on the edges define feasibility. The motivation comes from wireless networking, where we seek to model the irregularities seen in actual wireless environments. Not all node pairs may be able to communicate, even if geographically close --- thus, the available pairs are modeled with a link graph $\mathcal{L}=(V,E)$. Also, signal attenuation need not follow a nice geometric formulas --- hence, interference is modeled by a conflict (hyper)graph $\mathcal{C}=(E,F)$ on the links. The objective is to maximize the efficiency of the communication, or equivalently minimizing the length of a schedule of the tree edges in the form of a coloring. We find that in spite of all this generality, the problem can be approximated linearly in terms of a versatile parameter, the inductive independence of the interference graph. Specifically, we give a simple algorithm that attains a $O(ρ\log n)$-approximation, where $n$ is the number of nodes and $ρ$ is the inductive independence, and show that near-linear dependence on $ρ$ is also necessary. We also treat an extension to Steiner trees, modeling multicasting, and obtain a comparable result. Our results suggest that several canonical assumptions of geometry, regularity and "niceness" in wireless settings can sometimes be relaxed without a significant hit in algorithm performance.